&amp;#147;The Godfather&amp;#148; vs. &amp;#147;Chaos&amp;#148;: Comparing Linguistic Analysis Based on On-line Knowledge Sources and Bags-of-N-Grams for Movie Review Valence Estimation

Schuller, Björn; Schenk, Joachim; Rigoll, Gerhard; Knaup, Tobias

doi:10.1109/icdar.2009.194

Cited by 16 publications

(12 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[183,22]. In addition, exploitation of on-line knowledge sources without domain specific model training has recently become an interesting alternative or addition [190] -e. g., to cope with out-of-vocabulary events.…”

Section: Linguistic Featuresmentioning

confidence: 99%

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Schuller

Batliner

Steidl

et al. 2011

Speech Communication

Self Cite

606

329

View full text Add to dashboard Cite

More than a decade has passed since research on automatic recognition of emotion from speech has become a new field of research in line with its 'big brothers' speech and speaker recognition. This article attempts to provide a short overview on where we are today, how we got there and what this can reveal us on where to go next and how we could arrive there. In a first part, we address the basic phenomenon reflecting the last fifteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then shift to automatic processing including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the first comparative challenge on emotion recognition from speech -the INTERSPEECH 2009 Emotion Challenge, organised by (part of) the authors, including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the actual learnt lessons before we finally address the ever-lasting problems and future promising attempts.Keywords: emotion, affect, automatic classification, feature types, feature selection, noise robustness, adaptation, standardisation, usability, evaluation Setting the SceneThis special issue will address new approaches towards dealing with the processing of realistic emotions in speech, and this overview article will give an account of the state-of-the-art, of the lacunas in this field, and of promising approaches towards overcoming shortcomings in modelling and recognising realistic emotions. We will also report on the first emotion challenge at INTERSPEECH 2009, constituting the initial impetus of this special issue; to end with, we want to sketch future strategies and applications, trying to answer the question 'Where to go from here?'The article is structured as follows: we first deal with the basic phenomenon briefly reflecting the last fifteen years, commenting on databases, modelling and annotation, the unit of analysis and prototypicality. We then proceed to automatic processing (sec. 2) including discussions on features, classification, robustness, evaluation, and implementation and system integration. From there we go to the the first Emotion Challenge (sec. 3) including the description of the Challenge's database, Sub-Challenges, participants and their approaches, the winners, and the fusion of results to the lessons learnt, before concluding this article (sec. 4).

show abstract

Section: Linguistic Featuresmentioning

confidence: 99%

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Schuller

Batliner

Steidl

et al. 2011

Speech Communication

Self Cite

606

329

View full text Add to dashboard Cite

show abstract

“…Smaragdis & Brown 2003). For linguistic analysis, on-line knowledge sources can further be exploited to find the mood conveyed, such as word relations (Liu & Singh, 2004;Schuller, Schenk, Rigoll, & Knaup, 2009). Likewise words that have never been seen in a training can be looked up.…”

Section: Discussionmentioning

confidence: 99%

‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification

Schuller

Hage

Schuller³

et al. 2010

Journal of New Music Research

Self Cite

View full text Add to dashboard Cite

Mass consumption of large collections of digital music asks for efficient and intuitive ways of organization. In this article, a system is presented which recognizes the evoked music mood on the basis of a wide variety of features, closely sticking to real world conditions. A twodimensional mood model is discussed in which moods resemble binary values for arousal and valence and an easy and thus user-friendly method is presented through which a fuzzy seven-class mood cluster is deducted. The songs of the 'Twenty Years of MTV Europe Most Wanted' music database consisting of recorded pop music tracks serve for evaluation of three groups of features: firstly, traditional features such as rhythm and tonal features, zero crossing rate, cepstral, and MPEG-7 Low Level Descriptors for audio content are extracted. Secondly, lyrics, chord sequences, and genre data are obtained from on-line sources. Thirdly, from all these, the high-level features, musical mode, and as a novel feature, the suited ballroom dance style, are created automatically. The features selected are data-driven, and Support Vector Machines are used for classification. Prediction accuracies of 77.4% for arousal and 72.9% for valence as well as 71.8% (including neighbours) for the seven-class cluster model are obtained pre-serving realism in terms of non-prototypical music selection and feature extraction throughout.

show abstract

“…We can sort the textual features we use into 4 groups : -The N-grams features : The BoNG presented in [19] is an extension of the classical Bag of Words representation to N-grams. In this work we use words, bi-grams and tri-grams.…”

Section: Featuresmentioning

confidence: 99%

“…[15] used BoW and SVM for a sentiment analysis task over the MOUD dataset (Vlogs) obtaining a score of 64.94%. The Bag-of-N-grams (BoNG), which is an extension of this model, was used by [19] for a sentiment analysis task over the Metacritic database (textual movie reviews). [23] merge the results of subjectivity lexicons, valence shifters and BoNG to train a classifier for sentiment analysis in tweets.…”

Section: Introductionmentioning

confidence: 99%

Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields

2017

View full text Add to dashboard Cite

In this paper, the main goal is to detect a movie reviewer's opinion using hidden conditional random fields. This model allows us to capture the dynamics of the reviewer's opinion in the transcripts of long unsegmented audio reviews that are analyzed by our system. High level linguistic features are computed at the level of inter-pausal segments. The features include syntactic features, a statistical word embedding model and subjectivity lexicons. The proposed system is evaluated on the ICT-MMMO corpus. We obtain a F1-score of 82%, which is better than logistic regression and recurrent neural network approaches. We also offer a discussion that sheds some light on the capacity of our system to adapt the word embedding model learned from general written texts data to spoken movie reviews and thus model the dynamics of the opinion.

show abstract

The Godfather vs. Chaos: Comparing Linguistic Analysis Based on On-line Knowledge Sources and Bags-of-N-Grams for Movie Review Valence Estimation

Cited by 16 publications

References 6 publications

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge

‘Mister D.J., Cheer Me Up!’:Musical and Textual Features for Automatic Mood Classification

Opinion Dynamics Modeling for Movie Review Transcripts Classification with Hidden Conditional Random Fields

Contact Info

Product

Resources

About